Venue
International Journal of Innovative Science and Research Technology
Domain
Healthcare, Artificial Intelligence, Medical Informatics
Comprehensive clinical documentation is crucial for effective healthcare delivery, yet it poses a significant burden on healthcare professionals, leading to burnout, increased medical errors, and compromised patient safety.This paper explores the potential of generative AI (Artificial Intelligence) to streamline the clinical documentation process, specifically focusing on generating SOAP (Subjective, Objective, Assessment, Plan) and BIRP (Behavior, Intervention, Response, Plan) notes.We present a case study demonstrating the application of natural language processing (NLP) and automatic speech recognition (ASR) technologies to transcribe patient-clinician interactions, coupled with advanced prompting techniques to generate draft clinical notes using large language models (LLMs).The study highlights the benefits of this approach, including time savings, improved documentation quality, and enhanced patient-centered care.Additionally, we discuss ethical considerations, such as maintaining patient confidentiality and addressing model biases, underscoring the need for responsible deployment of generative AI in healthcare settings.The findings suggest that generative AI has the potential to revolutionize clinical documentation practices, alleviating administrative burdens and enabling healthcare professionals to focus more on direct patient care.I.
This paper explores the application of generative AI in clinical documentation, focusing on the generation of SOAP and BIRP notes. It highlights the burden of documentation on healthcare professionals and proposes a case study that employs natural language processing (NLP) and automatic speech recognition (ASR) to streamline the process. The methodology involves data collection from simulated patient-clinician interactions, transcription of dialogues, and the use of various large language models (LLMs) to generate structured clinical notes. The study addresses ethical considerations, compares AI performance, and emphasizes the need for iterative note improvement and human oversight. Challenges such as data quality, privacy concerns, and regulatory compliance are discussed along with future research directions.
This paper employs the following methods:
- Natural Language Processing (NLP)
- Automatic Speech Recognition (ASR)
- Prompt Engineering
- Large Language Models (LLMs)
- ChatGPT-4
- GPT-3.5
- Claude V3
- Mixtral8x7b Instruct
- Llama-3 70B Instruct
The following datasets were used in this research:
- ROUGE-1
- Accuracy
- Precision
- Recall
- F1-score
- Demonstrated time savings in documentation
- Improved documentation quality
- Enhanced patient-centered care
The authors identified the following limitations:
- Number of GPUs: None specified
- GPU Type: None specified
Generative AI
Clinical documentation
SOAP notes
BIRP notes
Natural language processing
Automatic speech recognition
Large language models
AI in healthcare